Variable Selection in the Context of AI Fairness

2026-08-04

AI Summary

This study addresses the tendency of conventional variable selection methods in AI to overlook ethical and societal contexts, often introducing implicit biases that compromise fairness across subpopulations. To counter this, the authors propose a novel variable selection framework that systematically integrates mathematical modeling, ethical analysis, and regulatory compliance. Innovatively embedding philosophical ethics and social awareness into the variable selection process, the approach advocates retaining sensitive and potentially relevant variables—rather than discarding them outright—to enable fine-grained fairness assessments. Empirical results demonstrate that this strategy effectively reduces disparities among subgroups and enhances model trustworthiness and compliance with regulatory frameworks such as the European Union’s Artificial Intelligence Act.

Abstract

Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act. Traditional approaches often do not take into account philosophical ethics and social awareness. Variable selection processes, in particular, can introduce implicit bias, affecting equity across different subgroups. We discuss a mathematical approach that evaluates fairness in AI, aligning mathematical methodologies with ethical considerations and regulatory requirements. Our aim is to advocate for interdisciplinary collaboration to address fairness, emphasizing the importance of understanding broader ethical and societal contexts. Our approach emphasizes maintaining all potentially relevant variables to allow for more granular fairness assessments and to reduce implicit bias. The findings suggest that the exclusion of sensitive or critical variables may compromise equity between subgroups. In contrast, retaining all relevant variables could reduce implicit bias. Thus, the interdisciplinary approach could provide deeper insight into the ethical implications and compliance with regulatory standards. By integrating a mathematical approach with ethical and social awareness, we suggest more equitable outcomes and responsible AI deployment. This work underscores the necessity of interdisciplinary collaboration in effectively addressing fairness in AI systems aligned with the objectives of the European Union's AI Act, which seeks to promote trustworthy and fair AI systems.

Problem

• AI fairness
• variable selection
• implicit bias
• equity
• regulatory compliance

Innovation

• variable selection
• AI fairness
• implicit bias
• interdisciplinary approach
• EU AI Act